• Science and humanities are increasingly data-driven
  • Early-career training has not prepared all researchers for this

• Enable systematic, replicable and reproducible work
  • Design principles
    • Best practices for data
  • Software development methods
    • Automation of repetitive calculations

• What a computer does
  • A series of instructions
  • Data is piped through programs, and a result emerges

• What a researcher does
  • Exploring data, developing hypotheses, writing code, interpreting results
• Outputs include:
  • datasets, methods, teaching materials, software, papers, etc.

• Reproduced from Stoudt et al. (2021)

• Fundamentals of R and RStudio
• Fundamentals of programming (in R)
• Data management with the tidyverse
• Publication-quality data visualisation with ggplot2
• Reporting with RMarkdown

• R is:
  • a programming language
  • the software that interprets/runs programs written in the R language

• free (though commercial support can be bought)
• widely used
  • sciences, humanities, engineering, statistics, etc.
• has many excellent specialised packages for data analysis and visualisation
• international, friendly user community

• RStudio is an integrated development environment (IDE)

• Separates data from analysis
• Not point-and-click: every step is explicit and transparent
• Easy to share, adapt, reuse, publish analyses with new/modified data (GitHub)
• R can be run on supercomputers, with extremely large datasets…

• An item (object) of data goes in the box (which is called Name)
• When we refer to the box (variable) by its name, we really mean what’s in the box

x <- 1 / 40
x

## [1] 0.025

x ^ 2

## [1] 0.000625

log(x)

## [1] -3.688879

name <- "Samia"
name

## [1] "Samia"

current_temperature = 28.6
subjectID = "GCF_00001236452.1"
GPS_Location = "54N, 36E"

• descriptive, but not too long
• letters, numbers, underscores, and periods ([a-zA-z0-9_.])
• cannot contain whitespace or start with a number (x2 is allowed, 2x is not)
• case sensitive (Weight is not the same as weight)
• do not reuse names of built-in functions
• Consistent style:
  • lower_snake, UPPER_SNAKE, lowerCamelCase, UpperCamelCase

• automate complicated tasks
• make code more readable and reusable

• Some functions are built-in (in base packages, e.g. sqrt(), lm(), plot())
• Groups of related functions can be imported as libraries

args(fname)            # arguments for fname
?fname                 # help page for fname
help(fname)            # help page for fname
??fname                # any mention of fname
help.search("text")    # any mention of "text"
vignette(fname)        # worked examples for fname
vignette()             # show all available vignettes

mass <- 47.5
age <- 122
mass <- mass * 2.3
age <- age - 20

• mass = 47.5, age = 102
• mass = 109.25, age = 102
• mass = 47.5, age = 122
• mass = 109.25, age = 122

• Use a single working directory per project/analysis
  • easier to move, share, and find files
  • use relative paths to locate files
• Treat raw data as read-only
  • keep in a separate subfolder (data?)
• Clean data ready for work
  • keep cleaned/modified data in separate folder (clean_data?)
• Consider output generated by analysis to be disposable
  • can be regenerated by running analysis/code